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$15 USD /timme

Flagga för INDIA

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Datum, tid och plats: 6:15 em

Gick med januari 29, 2016

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Pinal D.

@hjdsolution

4,0

(14 omdömen)

4,8

4,8

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82%

$15 USD /timme

Flagga för INDIA

gotri,

$15 USD /timme

100 %

Slutförda jobb

94 %

Inom budget

26 %

Inom tid

12 %

Återanställningsgrad

Sr. Software Eng(FullStack Architect & Developer)

Hello, I am having 11+ years of working experience in bigdata, AI/ML, Hadoop, spark, scala, java, Python, j2ee, spring, spring boot, spring security, hibernate, spring data jpa, jsp, jQuery, AJAX, MySQL DB, Oracle DB, maven, JSON, XML, rest/SOAP web services, batch processing, multi threading, java design patterns, UML, Java swing, spring batch, product development, Java security (JCA), Typescript, MongoDB, Microsservice, Jenkins, Bamboo, CI/CD, Angular 4/11, JHipster, Bootstrap 4, CSS, HTML 5 Cloud tech: AWS, PCF, Azure Also having experience with implementing unit and integration test cases, framework used for this: Junit, Mockito, SpringMVC Working experience with application server: apache tomcat, jboss. Working experience with repositories : GIT, CVS, SVN Build Tools: having good experience with Maven and Gradle.

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Portfolio

11510674

Mendix Splitwise Problem Solution

In the run-up to the next meeting, we would like to ask you to model a case in Mendix. The purpose of this task is to give you an opportunity for you to get to know Mendix further and assess whether it is a technology and development method that makes you happy to work with.

This is also an opportunity for you to demonstrate your technical skills by finding a solution using a language you’re familiar with. It’s entirely up to you how you implement this solution, and we will not penalize you for not fully completing the task. Please make sure you read the whole document before attempting the task.
Case
A group of friends often go out after doing winter sports. They don’t have a system in place to help them split their bills. As a result, what typically happens is one time, person A pays the bill, the next time person B pays the bill and so on. The problem is that no one knows how much money each person has paid and how much money they are owed. They would like to build an app to help

Mendix Splitwise Problem Solution

ML Housing price problem solved with EDA, Classification models, Regression models, Unsupervised learning (K means clustoring, Hirarchical Clustering), Feature selection & PCA.

The project consists of three primary components: Preprocessing and Exploratory Data Analysis, Unsu- pervised Learning, and Supervised Learning, that span the material covered throughout the course. You will work in groups of 2-3 students. Please send your group member information (student names and IDs) to Sahil Bhatt (sahil.bhatt@te no later than Friday, Sep 22, 2023 at 11:59 pm.
Students who do not form a group by this deadline will be assigned to groups randomly.
Project Components
1. Preprocessing and Exploratory Data Analysis Select a dataset to work on from the data repositories listed at the end of this document.
(a) Preprocessing:
(b) Exploratory Data Analysis (EDA).
i. Linear Regression,
ii. KNN Regression (with an arbitrary selection of "n neighbors" parameter),
iii. Decision Tree Regr

ML Housing Price - EDA, Supervised & Unsupervised Learning

ML Housing price problem solved with EDA, Classification models, Regression models, Unsupervised learning (K means clustoring, Hirarchical Clustering), Feature selection & PCA.

The project consists of three primary components: Preprocessing and Exploratory Data Analysis, Unsu- pervised Learning, and Supervised Learning, that span the material covered throughout the course. You will work in groups of 2-3 students. Please send your group member information (student names and IDs) to Sahil Bhatt (sahil.bhatt@te no later than Friday, Sep 22, 2023 at 11:59 pm.
Students who do not form a group by this deadline will be assigned to groups randomly.
Project Components
1. Preprocessing and Exploratory Data Analysis Select a dataset to work on from the data repositories listed at the end of this document.
(a) Preprocessing:
(b) Exploratory Data Analysis (EDA).
i. Linear Regression,
ii. KNN Regression (with an arbitrary selection of "n neighbors" parameter),
iii. Decision Tree Regr

ML Housing Price - EDA, Supervised & Unsupervised Learning

ML Housing price problem solved with EDA, Classification models, Regression models, Unsupervised learning (K means clustoring, Hirarchical Clustering), Feature selection & PCA.

The project consists of three primary components: Preprocessing and Exploratory Data Analysis, Unsu- pervised Learning, and Supervised Learning, that span the material covered throughout the course. You will work in groups of 2-3 students. Please send your group member information (student names and IDs) to Sahil Bhatt (sahil.bhatt@te no later than Friday, Sep 22, 2023 at 11:59 pm.
Students who do not form a group by this deadline will be assigned to groups randomly.
Project Components
1. Preprocessing and Exploratory Data Analysis Select a dataset to work on from the data repositories listed at the end of this document.
(a) Preprocessing:
(b) Exploratory Data Analysis (EDA).
i. Linear Regression,
ii. KNN Regression (with an arbitrary selection of "n neighbors" parameter),
iii. Decision Tree Regr

ML Housing Price - EDA, Supervised & Unsupervised Learning

ML Housing price problem solved with EDA, Classification models, Regression models, Unsupervised learning (K means clustoring, Hirarchical Clustering), Feature selection & PCA.

The project consists of three primary components: Preprocessing and Exploratory Data Analysis, Unsu- pervised Learning, and Supervised Learning, that span the material covered throughout the course. You will work in groups of 2-3 students. Please send your group member information (student names and IDs) to Sahil Bhatt (sahil.bhatt@te no later than Friday, Sep 22, 2023 at 11:59 pm.
Students who do not form a group by this deadline will be assigned to groups randomly.
Project Components
1. Preprocessing and Exploratory Data Analysis Select a dataset to work on from the data repositories listed at the end of this document.
(a) Preprocessing:
(b) Exploratory Data Analysis (EDA).
i. Linear Regression,
ii. KNN Regression (with an arbitrary selection of "n neighbors" parameter),
iii. Decision Tree Regr

ML Housing Price - EDA, Supervised & Unsupervised Learning

ML Housing price problem solved with EDA, Classification models, Regression models, Unsupervised learning (K means clustoring, Hirarchical Clustering), Feature selection & PCA.

The project consists of three primary components: Preprocessing and Exploratory Data Analysis, Unsu- pervised Learning, and Supervised Learning, that span the material covered throughout the course. You will work in groups of 2-3 students. Please send your group member information (student names and IDs) to Sahil Bhatt (sahil.bhatt@te no later than Friday, Sep 22, 2023 at 11:59 pm.
Students who do not form a group by this deadline will be assigned to groups randomly.
Project Components
1. Preprocessing and Exploratory Data Analysis Select a dataset to work on from the data repositories listed at the end of this document.
(a) Preprocessing:
(b) Exploratory Data Analysis (EDA).
i. Linear Regression,
ii. KNN Regression (with an arbitrary selection of "n neighbors" parameter),
iii. Decision Tree Regr

ML Housing Price - EDA, Supervised & Unsupervised Learning

ML Housing price problem solved with EDA, Classification models, Regression models, Unsupervised learning (K means clustoring, Hirarchical Clustering), Feature selection & PCA.

The project consists of three primary components: Preprocessing and Exploratory Data Analysis, Unsu- pervised Learning, and Supervised Learning, that span the material covered throughout the course. You will work in groups of 2-3 students. Please send your group member information (student names and IDs) to Sahil Bhatt (sahil.bhatt@te no later than Friday, Sep 22, 2023 at 11:59 pm.
Students who do not form a group by this deadline will be assigned to groups randomly.
Project Components
1. Preprocessing and Exploratory Data Analysis Select a dataset to work on from the data repositories listed at the end of this document.
(a) Preprocessing:
(b) Exploratory Data Analysis (EDA).
i. Linear Regression,
ii. KNN Regression (with an arbitrary selection of "n neighbors" parameter),
iii. Decision Tree Regr

ML Housing Price - EDA, Supervised & Unsupervised Learning

ML Housing price problem solved with EDA, Classification models, Regression models, Unsupervised learning (K means clustoring, Hirarchical Clustering), Feature selection & PCA.

The project consists of three primary components: Preprocessing and Exploratory Data Analysis, Unsu- pervised Learning, and Supervised Learning, that span the material covered throughout the course. You will work in groups of 2-3 students. Please send your group member information (student names and IDs) to Sahil Bhatt (sahil.bhatt@te no later than Friday, Sep 22, 2023 at 11:59 pm.
Students who do not form a group by this deadline will be assigned to groups randomly.
Project Components
1. Preprocessing and Exploratory Data Analysis Select a dataset to work on from the data repositories listed at the end of this document.
(a) Preprocessing:
(b) Exploratory Data Analysis (EDA).
i. Linear Regression,
ii. KNN Regression (with an arbitrary selection of "n neighbors" parameter),
iii. Decision Tree Regr

ML Housing Price - EDA, Supervised & Unsupervised Learning

ML Housing price problem solved with EDA, Classification models, Regression models, Unsupervised learning (K means clustoring, Hirarchical Clustering), Feature selection & PCA.

The project consists of three primary components: Preprocessing and Exploratory Data Analysis, Unsu- pervised Learning, and Supervised Learning, that span the material covered throughout the course. You will work in groups of 2-3 students. Please send your group member information (student names and IDs) to Sahil Bhatt (sahil.bhatt@te no later than Friday, Sep 22, 2023 at 11:59 pm.
Students who do not form a group by this deadline will be assigned to groups randomly.
Project Components
1. Preprocessing and Exploratory Data Analysis Select a dataset to work on from the data repositories listed at the end of this document.
(a) Preprocessing:
(b) Exploratory Data Analysis (EDA).
i. Linear Regression,
ii. KNN Regression (with an arbitrary selection of "n neighbors" parameter),
iii. Decision Tree Regr

ML Housing Price - EDA, Supervised & Unsupervised Learning

2 factor authentication in keycloak project - need the authentication via email and sms.

Keyclock 2FA security with SMS and Email

2 factor authentication in keycloak project - need the authentication via email and sms.

Keyclock 2FA security with SMS and Email

2 factor authentication in keycloak project - need the authentication via email and sms.

Keyclock 2FA security with SMS and Email

Covid Hadoop Project

Covid Hadoop Project

IMDB Hadoop Project

: You need to compute the following for the given data using the
map/reduce paradigm. Try to compare and understand how you would do it using RDBMS if these
files were stored as relations. This may help you understand when map/reduce is meaningful and
when to use a RDBMS.
i. [PROJECT 3 – 100 points] There are many instances when a person directs a movie, TV
series etc., in which he also acts. For example, Ben Affleck directed and starred in the 2012
movie Argo. You need to do this for 3 title types (movie should be one of them) and 3 genres
chosen by you. There are about 20+ genres altogether. Write a Map/Reduce program to list
the names of people, who have directed and acted in the same IMDb title (of one of the
three genres chosen) along with title, and year.
Hint: This problem corresponds to a typical SQL query which has joins, group by and having
clauses. The purpose of this bonus problem is to understand how some of the relational
computations can

IMDB Hadoop Project

RSVP meetup streaming is spark streaming project used to listen stream object of meetup , process it (group, agreegation etc) and store it in hadoop.

RSVP meetup streaming

11510674

Mendix Splitwise Problem Solution

In the run-up to the next meeting, we would like to ask you to model a case in Mendix. The purpose of this task is to give you an opportunity for you to get to know Mendix further and assess whether it is a technology and development method that makes you happy to work with.

This is also an opportunity for you to demonstrate your technical skills by finding a solution using a language you’re familiar with. It’s entirely up to you how you implement this solution, and we will not penalize you for not fully completing the task. Please make sure you read the whole document before attempting the task.
Case
A group of friends often go out after doing winter sports. They don’t have a system in place to help them split their bills. As a result, what typically happens is one time, person A pays the bill, the next time person B pays the bill and so on. The problem is that no one knows how much money each person has paid and how much money they are owed. They would like to build an app to help

Mendix Splitwise Problem Solution

ML Housing price problem solved with EDA, Classification models, Regression models, Unsupervised learning (K means clustoring, Hirarchical Clustering), Feature selection & PCA.

The project consists of three primary components: Preprocessing and Exploratory Data Analysis, Unsu- pervised Learning, and Supervised Learning, that span the material covered throughout the course. You will work in groups of 2-3 students. Please send your group member information (student names and IDs) to Sahil Bhatt (sahil.bhatt@te no later than Friday, Sep 22, 2023 at 11:59 pm.
Students who do not form a group by this deadline will be assigned to groups randomly.
Project Components
1. Preprocessing and Exploratory Data Analysis Select a dataset to work on from the data repositories listed at the end of this document.
(a) Preprocessing:
(b) Exploratory Data Analysis (EDA).
i. Linear Regression,
ii. KNN Regression (with an arbitrary selection of "n neighbors" parameter),
iii. Decision Tree Regr

ML Housing Price - EDA, Supervised & Unsupervised Learning

ML Housing price problem solved with EDA, Classification models, Regression models, Unsupervised learning (K means clustoring, Hirarchical Clustering), Feature selection & PCA.

The project consists of three primary components: Preprocessing and Exploratory Data Analysis, Unsu- pervised Learning, and Supervised Learning, that span the material covered throughout the course. You will work in groups of 2-3 students. Please send your group member information (student names and IDs) to Sahil Bhatt (sahil.bhatt@te no later than Friday, Sep 22, 2023 at 11:59 pm.
Students who do not form a group by this deadline will be assigned to groups randomly.
Project Components
1. Preprocessing and Exploratory Data Analysis Select a dataset to work on from the data repositories listed at the end of this document.
(a) Preprocessing:
(b) Exploratory Data Analysis (EDA).
i. Linear Regression,
ii. KNN Regression (with an arbitrary selection of "n neighbors" parameter),
iii. Decision Tree Regr

ML Housing Price - EDA, Supervised & Unsupervised Learning

ML Housing price problem solved with EDA, Classification models, Regression models, Unsupervised learning (K means clustoring, Hirarchical Clustering), Feature selection & PCA.

The project consists of three primary components: Preprocessing and Exploratory Data Analysis, Unsu- pervised Learning, and Supervised Learning, that span the material covered throughout the course. You will work in groups of 2-3 students. Please send your group member information (student names and IDs) to Sahil Bhatt (sahil.bhatt@te no later than Friday, Sep 22, 2023 at 11:59 pm.
Students who do not form a group by this deadline will be assigned to groups randomly.
Project Components
1. Preprocessing and Exploratory Data Analysis Select a dataset to work on from the data repositories listed at the end of this document.
(a) Preprocessing:
(b) Exploratory Data Analysis (EDA).
i. Linear Regression,
ii. KNN Regression (with an arbitrary selection of "n neighbors" parameter),
iii. Decision Tree Regr

ML Housing Price - EDA, Supervised & Unsupervised Learning

ML Housing price problem solved with EDA, Classification models, Regression models, Unsupervised learning (K means clustoring, Hirarchical Clustering), Feature selection & PCA.

The project consists of three primary components: Preprocessing and Exploratory Data Analysis, Unsu- pervised Learning, and Supervised Learning, that span the material covered throughout the course. You will work in groups of 2-3 students. Please send your group member information (student names and IDs) to Sahil Bhatt (sahil.bhatt@te no later than Friday, Sep 22, 2023 at 11:59 pm.
Students who do not form a group by this deadline will be assigned to groups randomly.
Project Components
1. Preprocessing and Exploratory Data Analysis Select a dataset to work on from the data repositories listed at the end of this document.
(a) Preprocessing:
(b) Exploratory Data Analysis (EDA).
i. Linear Regression,
ii. KNN Regression (with an arbitrary selection of "n neighbors" parameter),
iii. Decision Tree Regr

ML Housing Price - EDA, Supervised & Unsupervised Learning

ML Housing price problem solved with EDA, Classification models, Regression models, Unsupervised learning (K means clustoring, Hirarchical Clustering), Feature selection & PCA.

The project consists of three primary components: Preprocessing and Exploratory Data Analysis, Unsu- pervised Learning, and Supervised Learning, that span the material covered throughout the course. You will work in groups of 2-3 students. Please send your group member information (student names and IDs) to Sahil Bhatt (sahil.bhatt@te no later than Friday, Sep 22, 2023 at 11:59 pm.
Students who do not form a group by this deadline will be assigned to groups randomly.
Project Components
1. Preprocessing and Exploratory Data Analysis Select a dataset to work on from the data repositories listed at the end of this document.
(a) Preprocessing:
(b) Exploratory Data Analysis (EDA).
i. Linear Regression,
ii. KNN Regression (with an arbitrary selection of "n neighbors" parameter),
iii. Decision Tree Regr

ML Housing Price - EDA, Supervised & Unsupervised Learning

ML Housing price problem solved with EDA, Classification models, Regression models, Unsupervised learning (K means clustoring, Hirarchical Clustering), Feature selection & PCA.

The project consists of three primary components: Preprocessing and Exploratory Data Analysis, Unsu- pervised Learning, and Supervised Learning, that span the material covered throughout the course. You will work in groups of 2-3 students. Please send your group member information (student names and IDs) to Sahil Bhatt (sahil.bhatt@te no later than Friday, Sep 22, 2023 at 11:59 pm.
Students who do not form a group by this deadline will be assigned to groups randomly.
Project Components
1. Preprocessing and Exploratory Data Analysis Select a dataset to work on from the data repositories listed at the end of this document.
(a) Preprocessing:
(b) Exploratory Data Analysis (EDA).
i. Linear Regression,
ii. KNN Regression (with an arbitrary selection of "n neighbors" parameter),
iii. Decision Tree Regr

ML Housing Price - EDA, Supervised & Unsupervised Learning

ML Housing price problem solved with EDA, Classification models, Regression models, Unsupervised learning (K means clustoring, Hirarchical Clustering), Feature selection & PCA.

The project consists of three primary components: Preprocessing and Exploratory Data Analysis, Unsu- pervised Learning, and Supervised Learning, that span the material covered throughout the course. You will work in groups of 2-3 students. Please send your group member information (student names and IDs) to Sahil Bhatt (sahil.bhatt@te no later than Friday, Sep 22, 2023 at 11:59 pm.
Students who do not form a group by this deadline will be assigned to groups randomly.
Project Components
1. Preprocessing and Exploratory Data Analysis Select a dataset to work on from the data repositories listed at the end of this document.
(a) Preprocessing:
(b) Exploratory Data Analysis (EDA).
i. Linear Regression,
ii. KNN Regression (with an arbitrary selection of "n neighbors" parameter),
iii. Decision Tree Regr

ML Housing Price - EDA, Supervised & Unsupervised Learning

ML Housing price problem solved with EDA, Classification models, Regression models, Unsupervised learning (K means clustoring, Hirarchical Clustering), Feature selection & PCA.

The project consists of three primary components: Preprocessing and Exploratory Data Analysis, Unsu- pervised Learning, and Supervised Learning, that span the material covered throughout the course. You will work in groups of 2-3 students. Please send your group member information (student names and IDs) to Sahil Bhatt (sahil.bhatt@te no later than Friday, Sep 22, 2023 at 11:59 pm.
Students who do not form a group by this deadline will be assigned to groups randomly.
Project Components
1. Preprocessing and Exploratory Data Analysis Select a dataset to work on from the data repositories listed at the end of this document.
(a) Preprocessing:
(b) Exploratory Data Analysis (EDA).
i. Linear Regression,
ii. KNN Regression (with an arbitrary selection of "n neighbors" parameter),
iii. Decision Tree Regr

ML Housing Price - EDA, Supervised & Unsupervised Learning

2 factor authentication in keycloak project - need the authentication via email and sms.

Keyclock 2FA security with SMS and Email

2 factor authentication in keycloak project - need the authentication via email and sms.

Keyclock 2FA security with SMS and Email

2 factor authentication in keycloak project - need the authentication via email and sms.

Keyclock 2FA security with SMS and Email

Covid Hadoop Project

Covid Hadoop Project

IMDB Hadoop Project

: You need to compute the following for the given data using the
map/reduce paradigm. Try to compare and understand how you would do it using RDBMS if these
files were stored as relations. This may help you understand when map/reduce is meaningful and
when to use a RDBMS.
i. [PROJECT 3 – 100 points] There are many instances when a person directs a movie, TV
series etc., in which he also acts. For example, Ben Affleck directed and starred in the 2012
movie Argo. You need to do this for 3 title types (movie should be one of them) and 3 genres
chosen by you. There are about 20+ genres altogether. Write a Map/Reduce program to list
the names of people, who have directed and acted in the same IMDb title (of one of the
three genres chosen) along with title, and year.
Hint: This problem corresponds to a typical SQL query which has joins, group by and having
clauses. The purpose of this bonus problem is to understand how some of the relational
computations can

IMDB Hadoop Project

RSVP meetup streaming is spark streaming project used to listen stream object of meetup , process it (group, agreegation etc) and store it in hadoop.

RSVP meetup streaming

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Erfarenhet

Freelancer

dec. 2012 - Nuvarande

Full stack developer

Kontakta Pinal D. om ditt uppdrag

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